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107 lines
3.5 KiB
107 lines
3.5 KiB
5 months ago
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from typing import List, Optional, Tuple, Union
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import torch
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from torch import nn, Tensor
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def _cat(tensors: List[Tensor], dim: int = 0) -> Tensor:
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"""
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Efficient version of torch.cat that avoids a copy if there is only a single element in a list
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"""
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# TODO add back the assert
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# assert isinstance(tensors, (list, tuple))
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if len(tensors) == 1:
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return tensors[0]
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return torch.cat(tensors, dim)
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def convert_boxes_to_roi_format(boxes: List[Tensor]) -> Tensor:
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concat_boxes = _cat([b for b in boxes], dim=0)
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temp = []
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for i, b in enumerate(boxes):
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temp.append(torch.full_like(b[:, :1], i))
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ids = _cat(temp, dim=0)
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rois = torch.cat([ids, concat_boxes], dim=1)
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return rois
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def check_roi_boxes_shape(boxes: Union[Tensor, List[Tensor]]):
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if isinstance(boxes, (list, tuple)):
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for _tensor in boxes:
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torch._assert(
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_tensor.size(1) == 4, "The shape of the tensor in the boxes list is not correct as List[Tensor[L, 4]]"
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)
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elif isinstance(boxes, torch.Tensor):
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torch._assert(boxes.size(1) == 5, "The boxes tensor shape is not correct as Tensor[K, 5]")
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else:
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torch._assert(False, "boxes is expected to be a Tensor[L, 5] or a List[Tensor[K, 4]]")
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return
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def split_normalization_params(
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model: nn.Module, norm_classes: Optional[List[type]] = None
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) -> Tuple[List[Tensor], List[Tensor]]:
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# Adapted from https://github.com/facebookresearch/ClassyVision/blob/659d7f78/classy_vision/generic/util.py#L501
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if not norm_classes:
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norm_classes = [
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nn.modules.batchnorm._BatchNorm,
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nn.LayerNorm,
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nn.GroupNorm,
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nn.modules.instancenorm._InstanceNorm,
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nn.LocalResponseNorm,
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]
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for t in norm_classes:
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if not issubclass(t, nn.Module):
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raise ValueError(f"Class {t} is not a subclass of nn.Module.")
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classes = tuple(norm_classes)
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norm_params = []
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other_params = []
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for module in model.modules():
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if next(module.children(), None):
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other_params.extend(p for p in module.parameters(recurse=False) if p.requires_grad)
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elif isinstance(module, classes):
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norm_params.extend(p for p in module.parameters() if p.requires_grad)
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else:
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other_params.extend(p for p in module.parameters() if p.requires_grad)
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return norm_params, other_params
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def _upcast(t: Tensor) -> Tensor:
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# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
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if t.is_floating_point():
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return t if t.dtype in (torch.float32, torch.float64) else t.float()
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else:
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return t if t.dtype in (torch.int32, torch.int64) else t.int()
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def _upcast_non_float(t: Tensor) -> Tensor:
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# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
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if t.dtype not in (torch.float32, torch.float64):
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return t.float()
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return t
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def _loss_inter_union(
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boxes1: torch.Tensor,
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boxes2: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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x1, y1, x2, y2 = boxes1.unbind(dim=-1)
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x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
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# Intersection keypoints
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xkis1 = torch.max(x1, x1g)
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ykis1 = torch.max(y1, y1g)
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xkis2 = torch.min(x2, x2g)
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ykis2 = torch.min(y2, y2g)
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intsctk = torch.zeros_like(x1)
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mask = (ykis2 > ykis1) & (xkis2 > xkis1)
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intsctk[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask])
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unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsctk
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return intsctk, unionk
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